Systems and methods for baseline correction using non-linear normalization

ABSTRACT

Systems and methods are provided for calibrating emission data or other information signals collected during a polymerase chain reaction (PCR), amplification reaction, assay, process, or other reaction. Calibration of multiple detectable materials can be achieved during a single cycle or run, or during a plurality of runs of the reaction. A reading from every well, container, or other support region of a sample support does not have to be taken. Interpolation can be used to determine values for emission data or other information signals that were not taken, or are unknown, using detected emission data, or other detected information signals. By calibrating the detected emission data and the interpolated data, a more accurate reading of emission data or information signal can be obtained.

RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.13/070,273 filed Mar. 23, 2011, which is a continuation of U.S. patentapplication Ser. No. 12/022,087 filed Jan. 29, 2008 (now U.S. Pat. No.8,005,628), which claims priority to U.S. Provisional Patent ApplicationNo. 60/898,064 filed Jan. 29, 2007, each of which is incorporated hereinin its entirety.

BACKGROUND

Real-time polymerase chain reaction (RT-PCR) technology, as presentlypracticed, relies upon the accurate detection of fluorescent emissionsignals above an initial baseline. The baseline signal can represent acombination of spurious or unwanted signal contributions such as theresidual fluorescence contributed by the plastic or other material of asample plate, the fluorescence of a running buffer or other non-reactantliquid material, noise in the optical detector or detection electronics,or some other source of background signal noise or detection floor thatis not a product of the amplification or other reaction. In variousknown RT-PCR implementations, better accuracy in the detection of theamplification signal, and hence original sample quantity, is frequentlysought by characterizing the baseline floor over the first few PCRcycles, or pre-signal detection cycles, and then subtracting thebaseline from the detected emissions once an inflection point into theexponential region has been reached. In general, a RT-PCR emission orother amplification graph, chart, or profile typically displays threesections or regions: an initial baseline region, an exponential region,and a plateau region. An example of this is shown in the illustration inFIG. 1. The baseline region can display a linear, or approximatelylinear, or other form over the first several cycles, as reactionchemistries have not liberated enough marker dye to rise over thedetected background. The next, exponential region represents the rise ofamplification product over the noise or background floor, as the PCRreaction kinetics come into force. The plateau region typically exhibitsa final flattening or tapering of detected emission intensities, asreagents are exhausted. The combined amplification profile usuallyresembles a sigmoid or S-shape. Typically, RT-PCR systems determine athreshold cycle (C_(T)) which represents the cycle point at which theexponential threshold is reached. From that parameter the originalsample quantity can be back-calculated, using standard curves.

Known baselining techniques involve the adjustment or normalization ofthe detected emission signal by subtracting the identified baseline inthe first few cycles from the detected fluorescent intensities of theRT-PCR marker dyes in later cycles, to sharpen the accuracy of theabsolute value of detected emission data in the exponential and/orplateau regions of the amplification profile. Baselining that reliesupon a subtraction operation to perform normalization can, however,cause certain effects in the resulting modified or normalized data. Forone, if a baselining operation is performed on a per-filter or per-dyewavelength basis, the baselining operation can determine differentbaselines for different filters or dyes, which after subtraction fromthe emission data lead to differing results for different detectedchannels. For another, if individual wells of a sample plate or othersupport or container are individually processed to create separatebaselines on a per-well basis, the set of resulting baselined signalscan be at a different scale or level. Furthermore, known baseliningtechniques involve the initial computation of baseline levels over thefirst few cycles, before exponential or plateau-region reactions takesplace. Subtracting those baseline levels from a set of exponential orplateau-region data captured at a later point can introduceinaccuracies, for instance if the baseline level drifts over latercycles. A need exists for baseline and related techniques that addressthese and other issues.

SUMMARY

Systems and methods according to various embodiments of the presentteachings relate to techniques and platforms to capture, identify, andcharacterize the baseline level of detected emission data of anamplification reaction, and to normalize the detected intensity data inan identified exponential region, plateau region, or other region of thedetected data. According to various embodiments, the adjustment ornormalization of the emission data can be performed by dividing the rawdetected emission signal by the identified baseline, resulting in anormalized, scaled, or weighted representation of the emission signal.According to various embodiments, because each normalized signal canincrease from a normalized background level of unity or close to unity(since the initial amplification cycles show a detected signal equal tothe background or baseline signal), a uniform or consistent scale can becreated across different dyes, filters, wells, or plates. According tovarious embodiments, the division of the detected signal by theidentified baseline can be performed in real-time, so that the resultingadjusted or normalized signal is output as the RT-PCR operation or otheranalysis takes place.

FIGURES

FIG. 1 illustrates an exemplary PCR amplification profile or curve,according to various embodiments of the present teachings.

FIG. 2 illustrates a schematic of a PCR detection system, according tovarious embodiments of the present teachings.

FIG. 3 illustrates a normalized or adjusted PCR amplification profile orcurve, according to various embodiments of the present teachings.

FIG. 4 illustrates a flowchart of baseline processing, according tovarious embodiments of the present teachings.

DESCRIPTION

Various embodiments of the present teachings relate to systems andmethods for baseline correction or adjustment of RT-PCR or otheramplification curves, signatures, graphs, profiles, or data, using anon-linear or non-subtractive normalization process. According tovarious embodiments, an amplification curve, signature, graph, profile,or data can be received from detection of fluorescent emissions in aRT-PCR or other instrument. According to various embodiments, thecalibration systems and methods can be implemented in or applied toRT-PCR scanning systems or RT-PCR imaging systems, or other systems orplatforms. In some embodiments, systems and methods according to thepresent teachings can be applied to non-real-time PCR instruments.

According to various embodiments, RT-PCR or other processing can takeplace using a standard sample plate, such as a 96-well or other capacitymicrotitre well or plate. In some embodiments, each well or othercontainer or location in a plate or other platform can contain samples,for example, samples of DNA fragments or other material, to which one ormore spectrally distinct dye is attached for detection and analysis.According to various embodiments, a calibration, normalization, or otheradjustment can be performed to normalize, adjust, or otherwise increasethe consistency and/or accuracy of the readings taken from the samplewells. According to various embodiments, the normalization orcalibration can correct or compensate for variations due to or affectedby factors which include, for example, differences in signal strength,dye or sample concentrations, contaminations, spectral or amplitudedistortions, deviations in optical path, plate geometry, fluorescentnoise floor, sample population or size, or other variations or anomaliesthat can arise from dye-to-dye, well-to-well, plate-to-plate, orinstrument-to-instrument variations.

According to various embodiments, the normalization or calibration cancomprise adjusting detected emission signals to compensate foridentified background or baseline signal or signals in a RT-PCRamplification, or other reaction. According to various embodiments, thiscan permit correction or adjustment for background optical uniformity,utilizing a normalized amplification profile, signature, graph, curve,or data, based upon the baseline of the detected PCR or other readings.According to various embodiments, the normalization can be carried outusing an endpoint of the PCR emission data, in addition to, or insteadof, the initial baseline. According to various embodiments, calibrationor normalization can be conducted in real-time, as the emission datafrom the PCR or other amplification or other process is detected.Herein, the term “emission” is used to exemplify a signal detectedand/or calibrated according to various embodiments of the presentteachings. It is to be understood that by “emission” the presentteachings are referring to not only electromagnetic radiation but ratherare also referring to any physical or chemical signal or other data thatcan be read, detected, imaged, or surmised from one or more area ofinterest, for example, a support region such as a well of a multi-wellplate. “Emission” herein is intended to encompass electromagneticradiation, optical signals, chemiluminescent signals, fluorescentsignals, radiation transmission values, and radiation absorption values.

According to various embodiments, a background reading can be taken withno dyes, samples, background samples, or other material present in thesample plate or other support. In some embodiments, a background sampleis used that comprises the same PCR mixture as is used on actual runs,but without the dyes. The background sample can mimic the actual runtime background. For example, the background emission of a plate havingdry or empty wells can be detected to determine baseline signal orsignals caused by residual fluorescent contributions from the materialof the plate itself, for example, from plastic or other material.Knowledge of the dry-plate contribution can also be used to determine ifany other factors are contributing to the noise floor or detectablebackground which can be present in the system, or to quantify thatremaining contribution.

According to various embodiments, background normalization andcorrection can be performed in connection with a RT-PCR system, such as,for instance, an overall system schematically illustrated in FIG. 2.According to embodiments as shown, a RT-PCR system can comprises adetector system 184, such as a scanning or whole-plate imaging opticaldetection element which can comprise, for example, a photomultipliertube, CCD device, or other optical or other detection element. Accordingto various embodiments, the detector system 184 can communicate with aprocessor 186 which can communicate with an input module 188, an outputmodule 190, and/or storage 192, such as local or networked disk storage.According to various embodiments, the detector system 184 can scan orimage a sample plate 180, to detect the optical emission from a set ofsample wells 194, such as wells arranged in a standard 96, 384, or othercapacity array. According to various embodiments, sample wells 194 cancontain samples in mixture with reagents to conduct a RT-PCR run. Insome embodiments, the RT-PCR processing can comprise operating thesystem at a series of RT-PCR temperatures regulated by thermal cyclerblock 182 and other electronic and thermal components, to subject thereactants in sample wells 194 to a desired sequence of denaturing,annealing, extension, and other steps.

According to various embodiments, and as illustrated, for instance, inFIG. 3, the output of a RT-PCR run can comprise a set of detectedemission data 210, generally representing detecting intensities offluorescent or other markers identifying PCR amplification products.According to various embodiments, emission data 210 can comprisediscrete values. In some embodiments, emission data 210 can comprisediscrete values that are interpolated, re-sampled or oversampled, toproduce a more dense, or differently-spaced, collection of data points.In some embodiments, emission data 210 can comprise a continuous curveor trace. According to various embodiments, emission data can extendover a total number of cycles from 1 to N, where N can be the endpointof a RT-PCR run, such as 30, 35, 40, or another number of cycles.According to various embodiments, the horizontal axis of theillustrative emission signature or profile shown in FIG. 3 can comprisecycle numbers, or it can comprise time units. According to variousembodiments, the vertical axis can comprise absolute or relativeamplitude or intensity units, or other measures. In some embodiments,the vertical axis can, for example, reflect detected emission orintensity values a on a logarithmic scale.

According to various embodiments, the baseline correction can comprisethe amplitude readings detected and received in the first few cycles ofa RT-PCR or other reaction, to isolate the initial cycles in whichamplification product is not yet detectable. According to variousembodiments, in the context of RT-PCR processes, the beginning and endcycles, which can form a candidate interval for defining the baselineregion, can be on the order of cycles 1 through 8, respectively, orlower or higher cycles. According to various embodiments, mathematicaltests can be applied to the detected signal in the first several cyclesof RT-PCR operation to determine the baseline region 212, such asdetermining a set of cycles over which the first derivative of thedetected signal remains below a predetermined threshold, or some otherthreshold. According to various embodiments, techniques such as thosedescribed in U.S. Pat. No. 7,228,237 to Woo et al., which isincorporated herein in its entirety by reference, or others, can be usedto isolate and identify the baseline region 212 and baseline signal 202located in the baseline region 212 of emission data 210.

According to various embodiments of the present teachings in one regard,and as, for example, also illustrated in FIG. 3, once the interval ofbaseline region 212 is identified and baseline signal 202 isolated,further normalization or adjustment of emission data 210 located in theremaining regions of emission data 210 can be performed. According tovarious embodiments in one regard, the normalization or other adjustmentcan comprise a division of the detected RT-PCR or other emission data210 in exponential region 214 (and/or plateau region 216) by thedetected baseline signal 202. According to various embodiments, this cangenerate a normalized amplification profile 204 in which the detectedemission signals in exponential region 214 and/or plateau region 216 arescaled, normalized, or otherwise adjusted to represent the ratio of thedetected signal in the respective region to the baseline signal 202.According to various embodiments, baseline signal 202 can comprise aconstant, non-varying, or scalar value. According to variousembodiments, normalized amplification profile 204 can be generated bydividing emission data 210 with constant 206, where baseline signal 202is determined to be a scalar or constant value.

According to various embodiments, baseline signal 202 can berepresented, encoded, or characterized by a time-varying function 208.According to various embodiments, function 208 can be or include alinear function, for instance, a linear function generated by performinga least-squares or other fitting operation on the data points in thefirst several cycles of the emission data. According to variousembodiments, function 208 can be or include a non-linear function, suchas a polynomial or other function. According to various embodiments, thedivision of the detected emission signals in exponential region 214(and/or plateau region 216) by baseline signal 202, which ischaracterized by a function 208, can produce normalized amplificationprofile 204 reflecting that ratio of functions. According to variousembodiments, the raw emission data 210, the baseline signal 202, thenormalized amplification profile 204, and other signals can each be acontinuous graph, function, or data set, or can be a discrete graph,function, or data set. According to various embodiments, normalizationgenerated by computing a ratio of emission data 210 over function 208can produce a normalized amplification profile 204, whose degree ofscaling varies along the cycle number (or time) axis, depending upon thevarying values of baseline signal 202 along that axis.

According to various embodiments, the normalized amplification profile204 can provide a compactly-scaled representation of the underlyingemission data when compared to the subtraction of a baseline value,since division of the emission data 210 by function 208 can reduce theoverall normalized range. According to various embodiments, normalizedamplification profile 204 can in one regard represent a more consistentbasis upon which to compare or calibrate different RT-PCR or other runs,because the dynamic range of each is expressed in terms of a ratio overbaseline.

According to various embodiments, the division of the detected emissiondata 210 from RT-PCR or other sources by baseline signal 202, in eitherthe form of constant 206 or function 208, can be performed in real-time,while emission data 210 are being detected, collected, and stored.According to various embodiments, the division or other normalizationoperation giving rise to normalized amplification profile 204 can beperformed after emission data 210 has been collected. According tovarious embodiments, the correction for baseline signal noise can alsoinclude other mathematical functions, treatments, computations oroperations, in addition to generating a ratio of emission data 210 overbaseline signal 202.

According to various embodiments, for example, after normalization bydivision of emission data 210 by baseline signal 202 as describedherein, normalized amplification profile 204 can be further normalizedor adjusted by, for example, subtracting a constant value, such as 1 orsome other value, from normalized amplification profile 204. Accordingto various embodiments, at the point that the detected emission data 210first rises above baseline signal 202, the ratio of those two quantitiescan be 1 or substantially close to 1. Subtracting 1 or some otheroffset, from the ratio initially generated by normalized amplificationprofile 204, can result in an overall amplification profile withdetected values increasing from a level of zero. According to variousembodiments, other further or alternative adjustments to normalizedamplification profile 204 can be made. According to various embodimentsin one regard, because the normalized amplification profile 204 can beconsistently scaled to starting points of 0, 1, or other desired levels,comparison, averaging, and other aggregate manipulation of the profilesgenerated by different wells, filters, dyes, samples, machines, or otherentities, can be uniformly performed. Therefore, a set of multiplenormalized amplification profiles generated according to the presentteachings can be employed to generate more useful and accuratecomparisons, make uniformity corrections, and other calibration oroperational measurements, between diverse machines, chemistries, orprocesses.

FIG. 4 illustrates a flowchart of overall baseline and emissionnormalization processing, according to various embodiments of thepresent teachings. In step 402, processing can begin. In step 404,emission data 210 from a RT-PCR or other amplification, or othermachine, instrument, or system can be detected or received. In step 406,a baseline region 212 in the emission data 210 can be identified, forinstance, based, for example, upon the greatest first derivative pointor other technique. In step 408, a constant 206 and/or function 208characterizing baseline signal 202 can be generated. In step 410, anormalized amplification profile 204 can be generated for theexponential region 214 and/or plateau region 216 of emission data 210.According to various embodiments, the normalized amplification profile204 can be generated by dividing emission data 210 by constant 206,function 208, a combination of the two, or some other quantity orparameter. In step 412, additional sets of emission data 210, forexample, emission intensities detected in additional RT-PCR or otherruns, can be normalized using the same techniques.

In step 414, the set of one or more normalized amplification profile 204can be compared, calibrated, or otherwise processed, for example, toperform uniformity calibration or analysis across different sampleplates, wells, dyes, samples, filters, machines, or other entities. Instep 416, any one or more normalized amplification profile 204, emissiondata 210, constant 206, function 208, or other data or information canbe stored, for example, to a local hard disk, network storage site, orother location or data store. In step 418, processing can repeat, returnto a prior processing point, proceed to a further processing point, orend.

Various embodiments of the present teachings can be implemented, inwhole or part, in digital electronic circuitry, or in computer hardware,firmware, software, or in combinations thereof. Apparatus of theinvention can be implemented in a computer program, software, code, oralgorithm embodied in machine-readable media, such as electronic memory,CD-ROM or DVD discs, hard drives, or other storage device or media, forexecution by a programmable processor. Various method steps according tothe present teachings can be performed by a programmable processorexecuting a program of instructions to perform functions and processesaccording to the present teachings, by operating on input data andgenerating output. The present teachings can, for example, beimplemented in one or more computer programs that are executable on aprogrammable system including at least one programmable processorcoupled to receive data and instructions from, and to transmit data andinstructions to, a data storage system or memory, at least one inputdevice such as a keyboard and mouse, and at least one output device,such as, for example, a display or printer. Each computer program,algorithm, software, or code can be implemented in a high-levelprocedural or object-oriented programming language, or in assembly,machine, or other low-level language if desired. According to variousembodiments, the code or language can be a compiled, interpreted, orotherwise processed for execution.

Various processes, methods, techniques, and algorithms can be executedon processors that can include, by way of example, both general andspecial purpose microprocessors, such as, for example, general-purposemicroprocessors such as those manufactured by Intel Corp. or AMD Inc.,digital signal processors, programmable controllers, or other processorsor devices. According to various embodiments, generally a processor willreceive instructions and data from a read-only memory and/or a randomaccess memory. According to various embodiments, a computer implementingone or more aspects of the present teachings can generally include oneor more mass storage devices for storing data files, such as magneticdisks, such as internal hard disks and removable disks, magneto-opticaldisks, and CD-ROM DVD, Blu-Ray, or other optical disks or media.

According to various embodiments, memory or storage devices suitable forstoring, encoding, or embodying computer program instructions orsoftware and data can include, for instance, all forms of volatile andnon-volatile memory, including for example semiconductor memory devices,such as random access memory, electronically programmable memory(EPROM), electronically erasable programmable memory, EEPROM, and flashmemory devices, as well as magnetic disks such as internal hard disksand removable disks, magneto-optical disks, and optical disks. Any ofthe foregoing can be supplemented by, or incorporated in, ASICs.According to various embodiments, processors, workstations, personalcomputers, storage arrays, servers, and other computer, information, orcommunication resources used to implement features of the presentteachings can be networked or network-accessible.

It will be appreciated that while various embodiments described aboveinvolve the calibration of one or more aspects of instrument reading,dye selection or preparation, or other factors, according to variousembodiments, more than one type of normalization or calibration can beperformed, together or in sequence. While the foregoing description hasgenerally described the normalization of the emission data as involvinggenerating a ratio of data to a baseline signal according to variousembodiments, the normalization can comprise, for example, dividing theemission data in the exponential region 214 and/or plateau region 216 bythe endpoint value of the RT-PCR run, after the amplification reactionis complete.

Other embodiments will be apparent to those skilled in the art fromconsideration of the present specification and practice of the presentteachings disclosed herein. It is intended that the presentspecification and examples be considered as exemplary only.

1-10. (canceled)
 11. A non-transitory computer-readable medium encodedwith instructions for generating calibration information for anamplification reaction, the instructions comprising instructions for:processing at least one calibration material in a sample support of athermal cycler instrument; performing an amplification reaction on asample in a sample support of the thermal cycler instrument; receivingfirst emission data generated by the at least one calibration materialduring the processing of the calibration material; receiving secondemission data generated by the sample during the amplification reactionof the sample; generating time-varying calibration information based onthe received first emission data; and correcting the received secondemission data as a function of the time-varying calibration informationon a real-time basis during the amplification reaction.
 12. Thecomputer-readable medium of claim 11, wherein the at least onecalibration material comprises at least one dye.
 13. Thecomputer-readable medium of claim 11, wherein the at least onecalibration material comprises at least one fluorescent dye.
 14. Thecomputer-readable medium of claim 11, wherein the step of processing theat least one calibration material comprises performing a polymerasechain reaction, and wherein the step of performing the amplificationreaction on the sample comprises performing a polymerase chain reaction.15. The computer-readable medium of claim 11, wherein the time-varyingcalibration information generated comprises at least one of a set ofmaximum values of the emission data, a set of minimum values of theemission data, and spectral response information derived from the firstemission data.
 16. The computer-readable medium of claim 11, wherein theprocessing of the calibration material is performed simultaneously withthe amplification reaction of the sample.
 17. The computer-readablemedium of claim 11, wherein the processing of the calibration materialis performed before the amplification reaction of the sample.
 18. Thecomputer-readable medium of claim 11, wherein the time-varyingcalibration information comprises a set of time-varying data pointscorresponding to discrete cycles in the amplification reaction.
 19. Thecomputer-readable medium of claim 11, wherein the time-varyingcalibration information comprises a time-varying function that applies acorrection to the received second emission data on a time-varying basisin real-time during the amplification reaction.
 20. A system forgenerating calibration information, comprising: a processor; and amemory encoded to: processing at least one calibration material in asample support of a thermal cycler instrument; performing anamplification reaction on a sample in a sample support of the thermalcycler instrument; receiving first emission data generated by the atleast one calibration material during the processing of the calibrationmaterial; receiving second emission data generated by the sample duringthe amplification reaction of the sample; generating time-varyingcalibration information based on the received first emission data; andcorrecting the received second emission data as a function of thetime-varying calibration information on a real-time basis during theamplification reaction.
 21. The system of claim 20, wherein the at leastone calibration material comprises at least one dye.
 22. The system ofclaim 20, further comprising at least one sample support for receivingthe calibration material and the at least one sample.
 23. The system ofclaim 20, wherein the input module unit is configured to receive firstemission data generated by at least one calibration material during apolymerase chain reaction.
 24. The system of claim 20, wherein theprocessor is configured to generate the time-varying calibrationinformation comprising at least one of a set of maximum values of thefirst emission data, a set of minimum values of the first emission data,and spectral response information derived from the first emission data.25. The system of claim 20, wherein the time-varying calibrationinformation comprises a set of time-varying data points corresponding todiscrete cycles in the amplification reaction.
 26. The system of claim20, wherein the time-varying calibration information comprises atime-varying function that applies a correction to the received secondemission data on a time-varying basis in real-time during theamplification reaction.